{"id":"https://openalex.org/W2810135932","doi":"https://doi.org/10.1145/3195570.3195580","title":"Gender bias in artificial intelligence","display_name":"Gender bias in artificial intelligence","publication_year":2018,"publication_date":"2018-05-28","ids":{"openalex":"https://openalex.org/W2810135932","doi":"https://doi.org/10.1145/3195570.3195580","mag":"2810135932"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3195570.3195580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026333045","display_name":"Susan Leavy","orcid":"https://orcid.org/0000-0002-3679-2279"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":true,"raw_author_name":"Susan Leavy","raw_affiliation_strings":["University College Dublin, Dublin, Ireland"],"affiliations":[{"raw_affiliation_string":"University College Dublin, Dublin, Ireland","institution_ids":["https://openalex.org/I100930933"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5026333045"],"corresponding_institution_ids":["https://openalex.org/I100930933"],"apc_list":null,"apc_paid":null,"fwci":11.602,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":205,"citation_normalized_percentile":{"value":0.99997,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"14","last_page":"16"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9607,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9607,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9527,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gender-bias","display_name":"Gender Bias","score":0.64304584},{"id":"https://openalex.org/keywords/disadvantage","display_name":"Disadvantage","score":0.58259517},{"id":"https://openalex.org/keywords/undo","display_name":"Undo","score":0.57437795},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.49026015}],"concepts":[{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.64304584},{"id":"https://openalex.org/C158071213","wikidata":"https://www.wikidata.org/wiki/Q7257","display_name":"Ideology","level":3,"score":0.626596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5893829},{"id":"https://openalex.org/C2777673361","wikidata":"https://www.wikidata.org/wiki/Q5281228","display_name":"Disadvantage","level":2,"score":0.58259517},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5818552},{"id":"https://openalex.org/C2780154230","wikidata":"https://www.wikidata.org/wiki/Q513420","display_name":"Undo","level":2,"score":0.57437795},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.55572087},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49026015},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43999267},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4342453},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4159978},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.33195588},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.32941198},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.25249654},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.11174694},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.083753794},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3195570.3195580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6,"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":29,"referenced_works":["https://openalex.org/W135881971","https://openalex.org/W1490029215","https://openalex.org/W1806647190","https://openalex.org/W1890106740","https://openalex.org/W1971387171","https://openalex.org/W1977884881","https://openalex.org/W1987695517","https://openalex.org/W2013143849","https://openalex.org/W2025388013","https://openalex.org/W2028324469","https://openalex.org/W2028331406","https://openalex.org/W2047176536","https://openalex.org/W2087541431","https://openalex.org/W2112611142","https://openalex.org/W2118862946","https://openalex.org/W2165272597","https://openalex.org/W2483215953","https://openalex.org/W2495956633","https://openalex.org/W2788481061","https://openalex.org/W2963453196","https://openalex.org/W2994617825","https://openalex.org/W3125978365","https://openalex.org/W4231906187","https://openalex.org/W4249223070","https://openalex.org/W4388116922","https://openalex.org/W51422847","https://openalex.org/W572474576","https://openalex.org/W584473771","https://openalex.org/W614192002"],"related_works":["https://openalex.org/W79743612","https://openalex.org/W4244323118","https://openalex.org/W2575202322","https://openalex.org/W2170927537","https://openalex.org/W2158967736","https://openalex.org/W2115968517","https://openalex.org/W2114343912","https://openalex.org/W2054873404","https://openalex.org/W1964568854","https://openalex.org/W1038420441"],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1,53,156],"is":[2,61,82,120,186],"increasingly":[3],"influencing":[4],"the":[5,15,20,89,93,141,149],"opinions":[6],"and":[7,43,128,176],"behavior":[8],"of":[9,17,22,29,72,87,92,114,125,143],"people":[10],"in":[11,19,31,77,122,148,154,183],"everyday":[12],"life.":[13],"However,":[14,51],"over-representation":[16],"men":[18],"design":[21],"these":[23],"technologies":[24],"could":[25],"quietly":[26],"undo":[27],"decades":[28,113],"advances":[30],"gender":[32,118,194],"equality.":[33],"Over":[34],"centuries,":[35],"humans":[36],"developed":[37],"critical":[38],"theory":[39],"to":[40,68,104,133,173,178,189],"inform":[41],"decisions":[42],"avoid":[44],"basing":[45],"them":[46],"solely":[47],"on":[48,116],"personal":[49],"experience.":[50],"machine":[52,134,184],"learns":[54],"primarily":[55,159],"from":[56,107,136,192],"observing":[57],"data":[58,73,81],"that":[59,80,162,196],"it":[60,130],"presented":[62],"with.":[63],"While":[64,99],"a":[65],"machine's":[66],"ability":[67],"process":[69],"large":[70],"volumes":[71],"may":[74],"address":[75],"this":[76,97,126],"part,":[78],"if":[79],"laden":[83],"with":[84],"stereotypical":[85],"concepts":[86],"gender,":[88],"resulting":[90],"application":[91],"technology":[94],"will":[95],"perpetuate":[96],"bias.":[98],"some":[100],"recent":[101],"studies":[102],"sought":[103],"remove":[105],"bias":[106,153,169],"learned":[108],"algorithms":[109,191],"they":[110],"largely":[111],"ignore":[112],"research":[115,127],"how":[117],"ideology":[119],"embedded":[121],"language.":[123],"Awareness":[124],"incorporating":[129],"into":[131],"approaches":[132],"learning":[135,185],"text":[137],"would":[138],"help":[139],"prevent":[140,190],"generation":[142],"biased":[144],"algorithms.":[145],"Leading":[146],"thinkers":[147],"emerging":[150],"field":[151],"addressing":[152],"artificial":[155],"are":[157,165,170],"also":[158],"female,":[160],"suggesting":[161],"those":[163],"who":[164],"potentially":[166],"affected":[167],"by":[168],"more":[171],"likely":[172],"see,":[174],"understand":[175],"attempt":[177],"resolve":[179],"it.":[180],"Gender":[181],"balance":[182],"therefore":[187],"crucial":[188],"perpetuating":[193],"ideologies":[195],"disadvantage":[197],"women.":[198]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2810135932","counts_by_year":[{"year":2024,"cited_by_count":42},{"year":2023,"cited_by_count":35},{"year":2022,"cited_by_count":56},{"year":2021,"cited_by_count":47},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-01-03T00:15:42.045587","created_date":"2018-07-10"}